Updated: 2020-08-27 07:26:52 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from \(log_2\)(\(R_e\)) > 0 to \(log_2\)(\(R_e\)) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

State Level Data


County Level Data


state R_e cases daily_cases
Connecticut 1.23 51916 132
Kansas 1.21 39945 630
North Dakota 1.20 10405 218
Michigan 1.17 109009 855
Iowa 1.16 58824 744
South Dakota 1.15 11436 153
Massachusetts 1.14 125313 367
Indiana 1.12 91347 1029
Rhode Island 1.12 19493 99
Montana 1.10 6705 114
Alabama 1.09 118768 1107
North Carolina 1.09 159892 1594
Colorado 1.08 56358 349
Illinois 1.05 226740 2060
Mississippi 1.05 80178 825
Minnesota 1.04 71279 665
Utah 1.04 50268 374
Kentucky 1.03 47535 666
Maine 1.03 4403 24
Oklahoma 1.03 54983 697
Louisiana 1.01 144808 742
Missouri 1.01 70244 1038
New Mexico 1.01 24719 136
South Carolina 1.01 114234 797
Nebraska 1.00 32531 237
Ohio 1.00 117615 942
Virginia 1.00 91423 684
West Virginia 1.00 9521 106
New Hampshire 0.99 7163 18
Wisconsin 0.99 72610 682
Georgia 0.98 242110 2463
Arkansas 0.97 57285 541
Oregon 0.97 25640 244
New York 0.96 436128 587
Vermont 0.96 1564 6
Maryland 0.95 106138 531
Tennessee 0.95 144418 1369
Wyoming 0.95 3683 39
Idaho 0.94 30952 303
Pennsylvania 0.93 135476 616
Washington 0.93 75308 522
Texas 0.91 620069 5470
California 0.89 688678 5812
New Jersey 0.88 191854 285
Nevada 0.85 67077 516
Arizona 0.84 199812 562
Florida 0.83 609188 3330
Delaware 0.67 16762 48

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

Mortality Trend

\(R_e\) Trend

National effective reproduction rate

Distribution of \(R_e\) Values

Howver, there is a wiude dirstubtion of \(R_e\) across regions and counties. The distributions in the graph below looks roughly symmetrical because the x-scale is logarithmic.

Distribution of Baseline Control

Similarly for disease control, when take at the county level, there is a wide distribution of Baseline Control.

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
Whitman WA 23 1 2.4 275 570 30
Grays Harbor WA 25 2 1.4 227 320 12
King WA 1 3 0.9 19091 880 131
Pierce WA 3 4 0.9 7336 850 56
Snohomish WA 4 5 0.9 6903 880 36
Spokane WA 5 6 0.9 5120 1030 33
Grant WA 9 7 0.9 2230 2350 34
Benton WA 6 8 1.0 4211 2170 18
Clark WA 8 11 0.9 2478 530 21
Yakima WA 2 14 0.8 11544 4630 24
Franklin WA 7 19 0.7 4011 4420 14
OR
county ST case rank severity R_e cases cases/100k daily cases
Coos OR 24 1 1.8 103 160 2
Multnomah OR 1 2 1.0 5813 730 51
Marion OR 2 3 1.0 3637 1080 44
Jackson OR 7 4 1.1 750 350 21
Washington OR 3 5 1.0 3635 620 31
Malheur OR 6 6 1.0 1080 3550 18
Clackamas OR 5 7 1.0 1868 460 19
Umatilla OR 4 8 0.9 2597 3380 15
Lane OR 9 10 0.9 666 180 4
Deschutes OR 8 12 0.9 678 380 4
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Los Angeles CA 1 1 0.9 235539 2330 1306
Sacramento CA 9 2 1.1 16757 1110 310
San Diego CA 5 3 1.0 37321 1130 264
Orange CA 3 4 0.9 47059 1490 344
Riverside CA 2 5 0.9 51606 2170 442
Ventura CA 16 6 1.1 10319 1220 114
San Bernardino CA 4 7 0.8 46308 2170 461
Fresno CA 7 8 0.9 24124 2470 339
Alameda CA 8 10 0.9 17465 1060 221
Kern CA 6 15 0.8 28714 3250 199

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 0.8 132622 3120 289
Pima AZ 2 2 0.9 20944 2050 133
Graham AZ 13 3 1.1 699 1850 11
Mohave AZ 6 4 0.9 3545 1720 17
Pinal AZ 4 5 0.8 9295 2210 41
Navajo AZ 5 6 1.0 5525 5080 8
Yuma AZ 3 7 0.8 12117 5830 21
Coconino AZ 8 8 0.9 3270 2330 9
Apache AZ 7 10 0.9 3318 4640 7
Santa Cruz AZ 9 12 0.9 2748 5900 4
CO
county ST case rank severity R_e cases cases/100k daily cases
Arapahoe CO 2 1 1.2 8081 1270 60
Douglas CO 8 2 1.4 2045 620 28
Adams CO 3 3 1.1 7373 1480 59
Denver CO 1 4 1.1 11046 1590 51
Summit CO 17 5 1.7 358 1180 2
Logan CO 14 6 1.6 671 3090 2
Jefferson CO 5 7 1.0 4668 820 27
Boulder CO 7 9 1.1 2259 700 12
El Paso CO 4 10 0.9 5901 860 37
Larimer CO 9 11 1.0 1854 550 17
Weld CO 6 12 1.0 3982 1350 16
UT
county ST case rank severity R_e cases cases/100k daily cases
Salt Lake UT 1 1 1.0 23274 2080 152
Utah UT 2 2 1.0 10432 1770 106
Millard UT 14 3 1.7 147 1150 2
Davis UT 3 4 1.1 3702 1090 33
Weber UT 4 5 1.0 3168 1280 23
Cache UT 6 6 1.1 2067 1690 10
Juab UT 15 7 1.3 104 950 3
Washington UT 5 9 0.9 2750 1710 14
Summit UT 7 11 1.0 833 2060 9
Tooele UT 9 13 1.0 657 1010 4
San Juan UT 8 17 1.0 663 4340 1
NM
county ST case rank severity R_e cases cases/100k daily cases
Sandoval NM 5 1 1.5 1216 860 8
Bernalillo NM 1 2 1.1 5675 840 35
Luna NM 15 3 1.5 275 1130 2
Santa Fe NM 8 4 1.1 798 540 10
Eddy NM 13 5 1.1 448 780 9
Chaves NM 11 6 1.0 647 990 11
Lea NM 7 7 0.9 1080 1540 15
San Juan NM 3 8 1.1 3163 2480 7
Doña Ana NM 4 9 0.8 2761 1280 11
McKinley NM 2 13 0.8 4169 5720 5
Otero NM 6 14 1.1 1118 1700 1
Cibola NM 9 16 0.8 726 2690 2

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Ocean NJ 7 1 1.2 11086 1870 30
Middlesex NJ 4 2 1.0 18551 2240 25
Sussex NJ 17 3 1.3 1414 990 4
Essex NJ 2 4 0.9 20417 2570 24
Passaic NJ 5 5 0.8 18386 3650 27
Burlington NJ 12 6 0.9 6361 1430 16
Camden NJ 9 7 0.8 9078 1790 20
Union NJ 6 8 0.9 17191 3110 15
Monmouth NJ 8 13 0.8 10722 1720 14
Bergen NJ 1 15 0.7 21665 2330 21
Hudson NJ 3 16 0.7 20246 3030 16
PA
county ST case rank severity R_e cases cases/100k daily cases
Columbia PA 28 1 1.5 540 820 7
Philadelphia PA 1 2 0.9 33262 2110 96
Berks PA 7 3 1.1 5918 1420 35
Montgomery PA 2 4 1.0 10830 1320 43
Allegheny PA 4 5 0.9 10045 820 58
Lancaster PA 6 6 1.0 6555 1220 35
Dauphin PA 13 7 1.0 3195 1160 23
Delaware PA 3 8 0.9 10203 1810 46
Chester PA 8 9 1.0 5540 1070 22
Bucks PA 5 13 0.9 7649 1220 22
Lehigh PA 9 40 0.7 5144 1420 6
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore MD 3 1 1.0 14975 1810 99
Prince George’s MD 1 2 0.9 26128 2880 99
Anne Arundel MD 5 3 1.0 8101 1430 45
Montgomery MD 2 4 0.9 19701 1890 65
Baltimore city MD 4 5 0.9 14214 2310 75
Wicomico MD 11 6 1.2 1488 1460 11
Worcester MD 14 7 1.3 778 1510 6
Frederick MD 7 9 1.0 3410 1370 19
Harford MD 8 10 0.9 2373 950 22
Howard MD 6 11 0.9 4293 1360 22
Charles MD 9 12 1.0 2318 1470 16
VA
county ST case rank severity R_e cases cases/100k daily cases
Goochland VA 60 1 2.0 194 860 4
Fairfax VA 1 2 1.0 17892 1560 92
Pulaski VA 74 3 1.6 119 350 4
Montgomery VA 34 4 1.4 401 410 10
Prince William VA 2 5 1.1 10526 2300 68
Fauquier VA 22 6 1.3 731 1060 11
Henrico VA 6 7 1.0 4428 1360 33
Virginia Beach city VA 3 10 0.9 5830 1300 43
Loudoun VA 4 13 1.0 5795 1500 29
Norfolk city VA 7 17 0.9 4257 1730 28
Chesterfield VA 5 20 0.9 4925 1450 29
Arlington VA 8 21 1.0 3441 1480 21
Newport News city VA 9 23 1.0 2178 1210 20
WV
county ST case rank severity R_e cases cases/100k daily cases
Monroe WV 29 1 2.6 91 680 15
Kanawha WV 1 2 1.0 1275 690 21
Grant WV 21 3 1.6 135 1160 1
Jefferson WV 7 4 1.3 331 590 3
Monongalia WV 2 5 1.1 1074 1020 9
Jackson WV 17 6 1.2 196 680 3
Putnam WV 12 7 1.0 256 450 4
Logan WV 5 11 0.7 464 1370 9
Berkeley WV 3 12 0.9 776 680 4
Cabell WV 4 20 0.7 501 530 3
Raleigh WV 6 21 0.6 332 440 3
Ohio WV 9 27 0.7 290 680 1
Wood WV 8 29 0.5 296 350 1
DE
county ST case rank severity R_e cases cases/100k daily cases
New Castle DE 1 1 0.9 7935 1430 39
Kent DE 3 2 0.5 2568 1470 6
Sussex DE 2 3 0.2 6259 2850 3

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Houston AL 15 1 1.5 1958 1880 52
Lee AL 9 2 1.4 3469 2180 56
Tuscaloosa AL 5 3 1.3 5283 2560 80
Dale AL 31 4 1.4 1049 2130 19
Henry AL 60 5 1.6 350 2040 8
Geneva AL 56 6 1.4 416 1570 14
Shelby AL 7 7 1.2 4072 1930 42
Jefferson AL 1 8 1.0 15388 2330 114
Mobile AL 2 17 0.9 11876 2860 70
Baldwin AL 6 18 1.0 4242 2040 34
Madison AL 4 25 1.0 6162 1720 34
Marshall AL 8 28 1.0 3583 3770 22
Montgomery AL 3 29 0.9 7708 3400 38
MS
county ST case rank severity R_e cases cases/100k daily cases
Issaquena MS 82 1 1.8 114 8580 13
Alcorn MS 49 2 1.6 567 1530 16
Rankin MS 6 3 1.2 2755 1820 37
Jackson MS 4 4 1.1 2874 2020 42
DeSoto MS 2 5 1.1 4427 2510 52
Lafayette MS 21 6 1.3 1225 2290 19
Bolivar MS 13 7 1.2 1405 4310 21
Harrison MS 3 9 1.0 3150 1550 38
Hinds MS 1 11 1.0 6375 2640 44
Madison MS 5 18 1.0 2841 2740 27
Lee MS 8 21 1.0 2088 2460 35
Forrest MS 9 22 1.1 2067 2740 16
Jones MS 7 24 1.1 2107 3080 13
LA
county ST case rank severity R_e cases cases/100k daily cases
Cameron LA 63 1 2.5 195 2840 5
Madison LA 44 2 2.2 710 6190 14
East Feliciana LA 38 3 1.8 878 4500 39
St. Helena LA 58 4 1.9 351 3370 11
St. Martin LA 19 5 1.6 1906 3550 18
Bossier LA 18 6 1.4 2625 2080 22
West Feliciana LA 50 7 1.4 520 3380 14
Jefferson LA 1 15 0.9 16231 3730 44
East Baton Rouge LA 2 16 0.8 13489 3040 58
Orleans LA 3 18 1.0 11265 2890 27
Caddo LA 6 20 0.9 7218 2910 28
St. Tammany LA 7 24 0.9 5902 2340 32
Ouachita LA 8 35 0.8 5409 3470 23
Lafayette LA 4 38 0.8 8190 3410 19
Calcasieu LA 5 40 0.8 7309 3650 20
Tangipahoa LA 9 43 0.7 3983 3050 20

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Miami-Dade FL 1 1 0.8 154496 5690 825
Hillsborough FL 4 2 0.9 36212 2630 184
Broward FL 2 3 0.8 70041 3670 310
Palm Beach FL 3 4 0.9 41090 2840 185
Orange FL 5 5 0.9 35132 2660 162
Duval FL 6 6 0.9 25900 2800 121
Charlotte FL 32 7 1.2 2584 1460 24
Polk FL 9 8 0.9 16532 2470 103
Lee FL 8 13 0.9 18239 2540 82
Pinellas FL 7 15 0.9 19646 2050 84
GA
county ST case rank severity R_e cases cases/100k daily cases
Bibb GA 10 1 1.6 5335 3480 185
Baldwin GA 34 2 1.5 1486 3280 46
Chattahoochee GA 47 3 1.5 1007 9350 26
Gwinnett GA 2 4 0.9 23666 2620 198
Cobb GA 3 5 1.0 16352 2190 146
Fulton GA 1 6 0.9 24289 2380 202
Clarke GA 20 7 1.2 2608 2090 40
Clayton GA 7 9 1.0 6255 2240 76
Hall GA 5 10 1.0 7217 3680 68
DeKalb GA 4 17 0.8 16238 2180 104
Chatham GA 6 29 0.9 6825 2380 52
Richmond GA 8 36 0.7 5822 2890 59
Muscogee GA 9 46 0.8 5367 2730 27

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Houston TX 100 1 2.8 460 2000 33
Madison TX 77 2 2.6 777 5500 21
Freestone TX 112 3 2.3 364 1850 38
Rusk TX 92 4 2.3 581 1080 37
Liberty TX 46 5 1.8 1592 1940 78
Fannin TX 97 6 1.8 520 1520 35
Taylor TX 38 7 1.7 2382 1750 68
Hidalgo TX 5 8 1.3 26246 3090 527
Cameron TX 7 9 1.4 20275 4810 283
Harris TX 1 11 1.0 101130 2200 906
Bexar TX 3 15 1.0 45678 2370 170
Dallas TX 2 20 0.6 73153 2830 560
Travis TX 6 23 0.8 26244 2180 164
Tarrant TX 4 25 0.7 40671 2010 263
Nueces TX 9 27 0.8 18477 5130 132
El Paso TX 8 33 0.7 19820 2370 124
OK
county ST case rank severity R_e cases cases/100k daily cases
Comanche OK 6 1 1.6 1213 990 48
Tulsa OK 2 2 1.0 12709 1980 135
Oklahoma OK 1 3 1.0 12925 1650 130
Payne OK 10 4 1.3 966 1190 20
Kingfisher OK 38 5 1.4 250 1600 11
Cleveland OK 3 6 1.1 3642 1320 43
Pottawatomie OK 12 7 1.1 796 1110 27
McCurtain OK 9 10 1.3 979 2970 10
Texas OK 7 13 1.3 1113 5270 5
Canadian OK 4 16 1.0 1452 1060 13
Wagoner OK 8 19 1.0 1088 1400 12
Rogers OK 5 38 0.7 1239 1360 10

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Luce MI 54 1 3.0 95 1490 17
Muskegon MI 11 2 2.0 1816 1050 72
Isabella MI 32 3 1.9 374 530 24
Wayne MI 1 4 1.1 30523 1730 156
Kent MI 4 5 1.2 8211 1280 52
Otsego MI 43 6 2.0 152 620 1
Macomb MI 3 7 1.1 12597 1450 115
Oakland MI 2 8 1.0 17577 1410 119
Washtenaw MI 6 10 1.2 3339 910 20
Saginaw MI 8 11 1.1 2438 1260 28
Genesee MI 5 14 1.1 3907 950 18
Jackson MI 7 27 1.2 2512 1580 6
Ottawa MI 9 36 0.9 2036 720 10
WI
county ST case rank severity R_e cases cases/100k daily cases
Brown WI 4 1 1.2 5136 1980 74
Fond du Lac WI 14 2 1.3 1021 1000 30
Green Lake WI 55 3 1.6 88 470 4
Marquette WI 57 4 1.9 84 550 0
Milwaukee WI 1 5 0.9 23564 2470 122
Outagamie WI 9 6 1.2 1636 890 28
Rock WI 7 7 1.3 1746 1080 15
Dane WI 3 8 1.0 5204 980 40
Waukesha WI 2 18 0.8 5373 1350 50
Racine WI 5 19 1.0 3895 1990 21
Walworth WI 8 28 0.9 1648 1600 16
Kenosha WI 6 32 0.9 2916 1730 12

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Hennepin MN 1 1 1.0 22116 1790 168
Ramsey MN 2 2 1.1 8802 1630 78
Dakota MN 3 3 1.0 5446 1300 65
Stearns MN 5 4 1.2 3147 2010 22
Washington MN 6 5 1.1 2727 1080 41
Itasca MN 41 6 1.5 172 380 3
Sibley MN 48 7 1.5 123 820 4
Anoka MN 4 8 1.0 4461 1280 46
Scott MN 8 18 0.9 1894 1320 18
Olmsted MN 7 25 0.9 1945 1270 11
Nobles MN 9 39 1.0 1835 8400 4
SD
county ST case rank severity R_e cases cases/100k daily cases
Clay SD 11 1 1.7 176 1260 6
Lawrence SD 12 2 1.6 131 520 8
Meade SD 10 3 1.5 186 680 11
Custer SD 18 4 1.5 97 1130 7
Pennington SD 2 5 1.3 1094 1000 19
Brookings SD 8 6 1.4 197 580 6
Minnehaha SD 1 7 0.9 4979 2670 33
Beadle SD 4 8 1.4 621 3380 3
Brown SD 5 9 1.1 570 1470 10
Codington SD 7 12 1.0 229 820 7
Lincoln SD 3 13 0.9 812 1480 10
Union SD 6 15 1.1 238 1570 2
Yankton SD 9 16 0.9 189 830 4
ND
county ST case rank severity R_e cases cases/100k daily cases
Grand Forks ND 3 1 1.6 1166 1660 56
Stutsman ND 10 2 1.7 147 700 3
McKenzie ND 16 3 1.7 107 850 2
Ward ND 6 4 1.3 448 650 21
Burleigh ND 2 5 1.1 1815 1940 45
Cass ND 1 6 1.2 3334 1910 23
Williams ND 7 7 1.3 346 1020 6
Stark ND 4 8 1.0 576 1870 19
Morton ND 5 10 1.0 564 1850 11
Benson ND 8 15 0.7 212 3080 3
Mountrail ND 9 17 0.6 163 1610 1

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
Hartford CT 3 1 1.5 13220 1480 46
Fairfield CT 1 2 1.2 18655 1980 48
New Haven CT 2 3 1.1 13546 1580 23
Litchfield CT 4 4 1.2 1661 910 3
Windham CT 8 5 1.2 782 670 3
New London CT 5 6 1.0 1524 570 4
Middlesex CT 6 7 1.0 1436 880 2
Tolland CT 7 8 0.9 1093 720 3
MA
county ST case rank severity R_e cases cases/100k daily cases
Suffolk MA 2 1 1.2 23074 2910 102
Middlesex MA 1 2 1.2 27302 1710 71
Essex MA 3 3 1.1 18639 2390 64
Berkshire MA 11 4 1.7 686 540 3
Worcester MA 4 5 1.1 14058 1710 33
Norfolk MA 5 6 1.1 10927 1560 23
Bristol MA 6 7 1.1 9644 1730 22
Plymouth MA 7 8 1.1 9522 1860 22
Hampden MA 8 9 1.0 7846 1670 17
Barnstable MA 9 10 1.4 1825 850 2
RI
county ST case rank severity R_e cases cases/100k daily cases
Newport RI 4 1 1.8 421 510 3
Providence RI 1 2 1.1 16428 2590 78
Bristol RI 5 3 1.7 338 690 2
Kent RI 2 4 1.3 1628 990 10
Washington RI 3 5 1.2 679 540 6

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
New York City NY 1 1 0.9 237566 2810 256
Erie NY 7 2 1.1 9591 1040 49
Chautauqua NY 28 3 1.5 306 240 6
Nassau NY 3 4 1.1 44393 3270 49
Broome NY 17 5 1.2 1268 650 11
Cayuga NY 43 6 1.6 173 220 2
Essex NY 48 7 1.3 118 310 6
Suffolk NY 2 8 0.9 44614 3000 43
Westchester NY 4 9 1.0 36776 3800 31
Dutchess NY 9 12 1.0 4800 1630 12
Rockland NY 5 16 0.9 14167 4380 14
Orange NY 6 17 0.9 11383 3010 12
Monroe NY 8 18 0.8 5372 720 18

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Rutland VT 4 1 1.6 106 180 1
Windham VT 3 2 1.4 120 280 2
Bennington VT 5 3 1.4 97 270 1
Chittenden VT 1 4 0.8 781 480 2
Franklin VT 2 5 0.6 123 250 0
Addison VT 6 6 0.4 77 210 0
Windsor VT 7 7 0.0 75 140 0
ME
county ST case rank severity R_e cases cases/100k daily cases
York ME 2 1 1.3 766 380 9
Kennebec ME 5 2 1.4 183 150 1
Androscoggin ME 3 3 1.0 597 560 2
Penobscot ME 4 4 0.8 226 150 4
Cumberland ME 1 5 0.7 2170 750 3
NH
county ST case rank severity R_e cases cases/100k daily cases
Cheshire NH 6 1 1.5 122 160 2
Rockingham NH 2 2 1.1 1778 580 6
Merrimack NH 3 3 1.2 496 330 3
Strafford NH 4 4 1.3 377 290 1
Grafton NH 7 5 1.2 110 120 0
Hillsborough NH 1 6 0.6 3995 970 5
Carroll NH 8 7 0.9 100 210 0
Belknap NH 5 8 0.4 122 200 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Greenville SC 2 1 1.3 11804 2370 69
Edgefield SC 41 2 1.5 485 1810 17
Pickens SC 16 3 1.4 2103 1710 23
Oconee SC 24 4 1.4 994 1300 15
Richland SC 3 5 1.0 10178 2490 84
Greenwood SC 17 6 1.2 1732 2460 22
Charleston SC 1 7 1.0 13503 3420 63
Spartanburg SC 6 10 1.0 4818 1590 40
Florence SC 9 12 0.9 4116 2970 36
Beaufort SC 8 13 1.0 4630 2530 28
Horry SC 4 15 0.9 9188 2860 31
Lexington SC 5 16 0.9 5549 1940 31
Berkeley SC 7 28 0.8 4663 2230 19
NC
county ST case rank severity R_e cases cases/100k daily cases
Orange NC 21 1 1.4 2097 1470 83
Pitt NC 13 2 1.4 2911 1640 86
Wake NC 2 3 1.3 14294 1370 186
Mecklenburg NC 1 4 1.0 24764 2350 158
Johnston NC 9 5 1.3 3730 1950 34
Guilford NC 4 6 1.1 6538 1250 61
Alamance NC 12 7 1.1 2969 1850 36
Forsyth NC 5 8 1.1 5956 1600 45
Cumberland NC 7 10 1.0 3856 1160 47
Union NC 8 12 1.0 3836 1690 44
Gaston NC 6 18 1.0 3917 1810 35
Durham NC 3 23 1.0 6732 2200 31

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Cascade MT 6 1 2.0 246 300 12
Glacier MT 10 2 1.6 124 910 6
Flathead MT 4 3 1.4 527 540 17
Yellowstone MT 1 4 1.0 1875 1190 38
Rosebud MT 9 5 1.2 161 1740 10
Ravalli MT 13 6 1.5 98 230 2
Lincoln MT 16 7 1.6 84 430 1
Gallatin MT 2 8 1.0 1049 1000 5
Big Horn MT 3 9 0.8 613 4580 8
Missoula MT 5 10 0.8 416 360 3
Lewis and Clark MT 8 12 0.6 193 290 1
Lake MT 7 15 0.6 194 650 0
WY
county ST case rank severity R_e cases cases/100k daily cases
Natrona WY 6 1 1.3 278 340 4
Laramie WY 2 2 1.1 556 570 4
Sheridan WY 11 3 1.1 130 430 5
Teton WY 3 4 1.1 416 1800 3
Uinta WY 5 5 1.3 286 1390 1
Campbell WY 8 6 1.0 169 350 2
Fremont WY 1 7 0.9 587 1460 5
Sweetwater WY 4 9 0.9 296 670 2
Park WY 9 10 0.8 161 550 1
Carbon WY 7 12 0.3 192 1240 2
ID
county ST case rank severity R_e cases cases/100k daily cases
Bonner ID 21 1 1.7 209 490 3
Payette ID 7 2 1.2 606 2630 18
Nez Perce ID 16 3 1.3 266 660 10
Latah ID 19 4 1.3 216 550 10
Ada ID 1 5 0.9 10882 2440 87
Canyon ID 2 6 0.9 6960 3280 55
Power ID 25 7 1.3 120 1560 6
Bonneville ID 4 9 0.9 1660 1480 28
Kootenai ID 3 10 1.0 2123 1380 14
Jerome ID 9 12 1.0 581 2480 6
Twin Falls ID 5 14 0.9 1626 1940 9
Bannock ID 6 16 0.8 628 740 8
Blaine ID 8 22 0.9 598 2720 1

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Henry OH 67 1 1.7 188 690 9
Jackson OH 74 2 1.6 151 460 10
Montgomery OH 5 3 1.2 5201 980 59
Summit OH 6 4 1.2 4273 790 53
Franklin OH 1 5 0.9 20956 1640 136
Meigs OH 81 6 1.6 96 410 4
Butler OH 7 7 1.1 3589 950 44
Cuyahoga OH 2 8 0.9 15200 1210 84
Hamilton OH 3 10 1.0 10726 1320 59
Lucas OH 4 11 1.0 6211 1440 50
Mahoning OH 9 22 1.0 2809 1220 14
Marion OH 8 53 1.0 2991 4580 3
IL
county ST case rank severity R_e cases cases/100k daily cases
Champaign IL 12 1 1.6 2157 1030 55
McLean IL 16 2 1.5 1246 720 65
Cook IL 1 3 1.0 123293 2360 737
Will IL 4 4 1.1 11110 1610 125
Fayette IL 61 5 1.6 136 630 8
DuPage IL 3 6 1.0 14132 1520 122
Lake IL 2 7 1.0 14141 2010 84
St. Clair IL 6 8 1.0 5575 2120 73
Kane IL 5 9 1.0 11016 2080 70
Madison IL 8 11 1.0 3753 1410 66
McHenry IL 9 13 1.1 3735 1210 33
Winnebago IL 7 24 1.1 4117 1440 25
IN
county ST case rank severity R_e cases cases/100k daily cases
St. Joseph IN 5 1 1.6 4755 1770 133
Monroe IN 22 2 1.8 979 670 29
Kosciusko IN 21 3 1.8 1009 1280 18
Lawrence IN 40 4 1.7 427 940 10
Marion IN 1 5 1.1 17984 1900 126
Martin IN 85 6 1.6 82 800 4
Allen IN 4 7 1.1 4774 1290 55
Hamilton IN 6 8 1.1 3661 1160 57
Lake IN 2 9 1.0 8878 1820 74
Hendricks IN 8 14 1.1 2236 1390 22
Elkhart IN 3 21 1.0 5530 2720 32
Vanderburgh IN 7 23 1.0 2414 1330 25
Johnson IN 9 25 1.1 2002 1320 14

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Sullivan TN 20 1 1.3 1543 980 44
Shelby TN 1 2 1.0 26684 2850 166
Lauderdale TN 46 3 1.4 681 2590 16
White TN 57 4 1.4 471 1770 17
Knox TN 5 5 1.0 6326 1390 80
Hamilton TN 3 6 0.9 7698 2150 83
Warren TN 33 7 1.2 777 1920 19
Davidson TN 2 10 0.8 25534 3730 111
Rutherford TN 4 15 0.9 7610 2480 52
Sumner TN 7 19 0.9 3948 2200 29
Bradley TN 9 26 0.9 2403 2300 25
Williamson TN 6 30 0.9 4222 1930 33
Wilson TN 8 34 0.9 2710 2040 21
KY
county ST case rank severity R_e cases cases/100k daily cases
Rowan KY 76 1 1.9 102 420 3
Warren KY 3 2 1.3 3107 2460 39
Jackson KY 45 3 1.6 191 1430 5
Jefferson KY 1 4 0.9 11687 1520 184
Green KY 63 5 1.5 131 1190 9
Madison KY 10 6 1.2 848 950 25
Fayette KY 2 7 1.0 5201 1630 77
Kenton KY 4 18 1.0 1668 1010 13
Daviess KY 6 21 1.0 942 940 11
Boone KY 5 24 1.0 1249 970 9
Hardin KY 8 25 1.0 873 810 12
Christian KY 9 40 0.9 850 1180 10
Shelby KY 7 49 0.8 881 1880 5

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Boone MO 7 1 1.3 2138 1210 66
St. Francois MO 14 2 1.3 972 1470 52
Greene MO 5 3 1.1 2754 950 88
Madison MO 72 4 1.5 87 710 7
St. Louis MO 1 5 0.9 18385 1840 175
Nodaway MO 37 6 1.4 311 1380 12
Jefferson MO 6 7 1.1 2567 1150 52
St. Charles MO 3 11 0.9 5442 1400 72
Jackson MO 4 13 0.9 5198 750 60
Clay MO 9 14 1.1 1306 550 18
Jasper MO 8 18 1.1 1500 1260 15
St. Louis city MO 2 19 0.9 6025 1940 40
AR
county ST case rank severity R_e cases cases/100k daily cases
Van Buren AR 64 1 1.8 101 610 7
Dallas AR 63 2 1.7 104 1400 5
Faulkner AR 10 3 1.4 1584 1290 25
Boone AR 39 4 1.5 285 760 8
Pulaski AR 1 5 1.0 6748 1720 69
Stone AR 51 6 1.4 177 1420 11
Poinsett AR 28 7 1.3 447 1860 13
Jefferson AR 5 8 1.0 1968 2790 26
Craighead AR 6 9 1.0 1740 1650 24
Benton AR 3 10 1.0 5146 1990 23
Sebastian AR 4 12 0.9 2761 2170 29
Washington AR 2 15 1.0 6664 2920 21
Pope AR 8 22 0.9 1631 2560 17
Crittenden AR 9 30 0.9 1608 3280 12
Hot Spring AR 7 56 0.5 1692 5050 4

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 1454.4 seconds to compute.
2020-08-27 07:51:06

version history

Today is 2020-08-27.
99 days ago: Multiple states.
91 days ago: \(R_e\) computation.
88 days ago: created color coding for \(R_e\) plots.
83 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
83 days ago: “persistence” time evolution.
76 days ago: “In control” mapping.
76 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
68 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
63 days ago: Added Per Capita US Map.
61 days ago: Deprecated national map.
57 days ago: added state “Hot 10” analysis.
52 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
50 days ago: added per capita disease and mortaility to state-level analysis.
38 days ago: changed to county boundaries on national map for per capita disease.
33 days ago: corrected factor of two error in death trend data.
29 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
24 days ago: added county level “baseline control” and \(R_e\) maps.
20 days ago: fixed normalization error on total disease stats plot.
13 days ago: Corrected some text matching in generating county level plots of \(R_e\).
7 days ago: adapter knot spacing for spline.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.